A recently-proposed machine learning approach to reference resolution --- the twin-candidate approach --- has been shown to be more promising than the traditional single-candidate approach. This paper presents a pronoun interpretation system that extends the twin-candidate framework by (1) equipping it with the ability to identify non-referential pronouns, (2) training different models for handling different types of pronouns, and (3) incorporating linguistic knowledge sources that are generally not employed in traditional pronoun resolvers. The resulting system, when evaluated on a standard coreference corpus, outperforms not only the original twin-candidate approach but also a state-of-the-art pronoun resolver.